The Manager / Senior Manager, Incubation does the core analytical and field work behind 2AI's next Big Bets. Reporting to the Incubation Director, you will own a portfolio of potential programs and pieces of the pipeline from first screen to pilot handoff. For each program you own, you will run the assessment end to end and make the recommendation on what advances, what pauses, and what is set aside.

This is a builder role in a start-up organization. The screening process, the deep-dive template, and the pilot playbook do not exist yet. You will help create them, then use them, then improve them.

• You have identified multiple AI Big Bets for 2AI to invest in at the pilot stage, each backed by an evidence review, a cost-effectiveness estimate, and a clear recommendation 2AI leadership could act on.
• You have helped design on-the-ground pilots that answer decision-relevant questions.
• You have built the AI-enabled workflows needed to accelerate and improve the Incubator's screening and deep-dive process, and that process is written down and in use across the team.

Own the case for (or against) potential Big Bets: an opportunity to use AI to improve the lives of millions of people in LMICs

• Own a portfolio of program due diligence end to end: take each AI-enabled intervention you own from long list to rapid screen to deep dive, and be accountable for the quality and speed of the assessment you recommend to leadership.
• Drive the thinking, not just the research: frame the decision-relevant question for each program, find the crux quickly, and decide what evidence would change the answer.
• Build the analysis that underpins each decision: evidence reviews, back-of-the-envelope (BOTEC) and full cost-effectiveness estimates, risk assessments, landscape scans, expert interviews, and early operational and technical scoping.
• Make the recommendation: write clear, calibrated memos to 2AI leadership on whether an idea should advance to pilot, pause, or be set aside, and say how confident you are and why.

Move winners from paper to pilot

• Co-design pilots that answer the decision-relevant question: working with 2AI leadership and program teams, define scope, sample, measurement, cost tracking, timeline, and success and stop criteria in advance. Favor fast, lean designs that answer the core question over comprehensive ones.
• Do the on-the-ground work: travel to prospective pilot locations to assess partners and delivery channels, negotiate scopes with implementers, and work in-country to stand up pilot infrastructure.
• Build the repeatable stack: data pipelines, monitoring dashboards, evaluation templates, and partner MOUs, so the second and third pilot designs are more efficient than the first.

Help build the Incubator itself

• Co-design the process: work with 2AI leadership to refine the screening criteria, define key activities and sequencing, clarify roles between teams, and build the supporting tools. Continually refine the process, based on what you learn from real cases.
• Make the Incubator AI-native: show how a small research team uses frontier AI tools (Claude Code, agentic pipelines, LLM-assisted literature synthesis) to work well beyond its size. Build, test, and document the AI workflows behind each key step in the Incubator process.
• Support recruitment: help source, assess, and onboard future Incubator team members as the team grows.

The ideal candidate has a track record of structured, rigorous problem solving in fast, project-based environments: taking in a lot of open-ended information, structuring a path to answer a specific question, and getting to a clear recommendation for stakeholders.

You have also used AI to meaningfully enhance your work, and are energized by the prospect of working to translate AI capabilities to practical applications. The role sits at the nexus of research, analytics, strategy and operations: it is for someone who wants to own the question "what should 2AI do next, and how do we know," and who enjoys being accountable for the answer.

• 3-4+ years of experience in management consulting, a rigorous startup environment, or a high-caliber NGO, including time leading a workstream from an open question to a recommendation.
• Deep, hands-on experience with modeling and quantitative analysis: building models from scratch, including back-of-the-envelope estimates under uncertainty, and pressure-testing other people's assumptions. Experience with cost-effectiveness analysis and its benchmarks (GiveWell, Coefficient Giving, DCP3, WHO-CHOICE, GiveDirectly cash as a bar) is a strong plus, but any rigorous modeling or quantitative work counts.
• The ability to read research critically: telling a strong randomized controlled trial (RCT) from a weak observational study, and knowing what a given piece of evidence can and cannot support. Experience with research methods and evidence reviews is a strong plus.
• Experience engaging with technical experts, in fields where you yourself are not an expert, to gather key information and insights.
• Experience working in a fast-paced, project-based environment with short deliverable cycles, sprints, and frequent stakeholder check-ins.
• The ability to hone in on the crux of an argument in a field you do not know well and to communicate confidence levels legibly in writing.
• Comfort with ambiguity, high throughput, and the pace of an early-stage organization.
• Willingness to travel internationally (up to 25%) to pilot sites and partners.
• Heavy, daily use of frontier AI tools (Claude, Claude Code, or equivalent) in your analytical work, and a track record of pushing how you use them further.
• Excellent written communication, especially short decision memos that lead with the recommendation and are honest about uncertainty.

• Experience working on digital product uptake and integration (product strategy, user needs, adoption, unit economics), ideally including AI products, and ideally in an LMIC context, in either the for-profit or non-profit sector.
• Experience working in LMIC contexts and/or working in the international development sector.
• Research or monitoring, learning, and evaluation (M&E) experience, or NGO program experience.
• Domain depth in a sector 2AI has not yet entered (education, financial inclusion, WASH, climate adaptation).

Position Location

This role location is flexible anywhere in North America for fully remote candidates. This role requires the ability to travel internationally up to 25% of the time. We are unable to sponsor or take over sponsorship of a U.S. employment visa at this time. Applicants must be legally authorized to work in the U.S. for roles based in the U.S.

The expected role range for this position is listed below. We consider multiple factors when determining the base salary for a role, including but not limited to: role scope, program budgets, internal equity, and a candidate's qualifications and/or prior experience.

Note: Pay and benefits will be commensurate with the role specifications, local statutory requirements, and the cost of labor in the markets where we operate.

The pay range for this role is: $100,000-135,000 USD.

While we are currently incubated at Evidence Action, 2AI plans to spin out on November 1, 2026 as an independent organization. New employees will be hired directly by 2AI. We will offer a comprehensive benefits package, including health, vision, and dental, retirement plans, generous and flexible leave options, as well as other employee perks.

Interview Process Expectations

Given this role will have a significant impact on our organization's early growth, we're focused on finding the right long-term fit while remaining as respectful of your time as possible. Our process is designed to explore both our shared culture and how we'd actually work together day-to-day.

Screening call: 30 mins - 1 hour

Performance task: 2 hours (practical exercise)

Team interviews: Two one-hour sessions with the people you'll work with most directly, including a short verbal case

Final interview: 1 hour

Reference checks